Christina Schneegass
Nađa Terzimehić
Stefan Schneegass
ABSTRACT
Smartphones enable people to learn new languages whenever and wherever they want. This popularized mobile language learning apps (MLLAs) and in particular micro learning that offers simple and short learning units to keep the user on track. Due to the ubiquitous use of these applications, they have to adapt to the users' current situation to provide an optimal learning experience. To gain insights into how users perceive common usage scenarios, we conducted an online survey (N=74) and clustered all described learning scenarios into five categories of usage situations. We outlined internal and contextual factors which are characteristic for these situations and discussed those in a follow-up focus group with HCI experts (N=4). During this focus group, we collected four design recommendations to adapt MLLAs to situations of users' (a) high attention levels, (b) tiredness or exhaustion, (c) highly demanding environments, or (d) low motivation.
- Rainer H. Kluwe, Spektrum Online Lexikon Psychologie. https://www.spektrum.de/lexikon/psychologie/kognition/7882 Last checked: 10.August, 2018.Google Scholar
- Brian P Bailey and Joseph A Konstan. 2006. On the need for attention-aware systems: Measuring effects of interruption on task performance, error rate, and affective state. Computers in human behavior 22, 4 (2006), 685--708.Google Scholar
- Peter A Bruck, Luvai Motiwalla, and Florian Foerster. 2012. Mobile Learning with Micro-content: A Framework and Evaluation.. In Bled eConference. 2.Google Scholar
- Tanis Bryan, Sarup Mathur, and Karen Sullivan. 1996. The impact of positive mood on learning. Learning Disability Quarterly 19, 3 (1996), 153--162.Google ScholarCross Ref
- Andreas Bulling. 2016. Pervasive attentive user interfaces. Computer 1 (2016), 94--98. Google ScholarDigital Library
- Andreas Bulling and Daniel Roggen. 2011. Recognition of visual memory recall processes using eye movement analysis. In Proceedings of the 13th international conference on Ubiquitous computing. ACM, 455--464. Google ScholarDigital Library
- Chih-Ming Chen, Jung-Ying Wang, and Chih-Ming Yu. 2017. Assessing the attention levels of students by using a novel attention aware system based on brainwave signals. British Journal of Educational Technology 48, 2 (2017), 348--369.Google ScholarCross Ref
- Fang Chen, Jianlong Zhou, Yang Wang, Kun Yu, Syed Z Arshad, Ahmad Khawaji, and Dan Conway. 2016. Robust multimodal cognitive load measurement. Springer. Google Scholar
- William L Cull and others. 2000. Untangling the benefits of multiple study opportunities and repeated testing for cued recall. Applied Cognitive Psychology 14, 3 (2000), 215--235.Google ScholarCross Ref
- Valérie Demouy, Ann Jones, Qian Kan, Agnes Kukulska-Hulme, and Annie Eardley. 2016. Why and How Do Distance Learners Use Mobile Devices for Language Learning? The EuroCALL Review 24, 1 (2016), 10--24.Google ScholarCross Ref
- Tilman Dingler, Albrecht Schmidt, and Tonja Machulla. 2017a. Building Cognition-Aware Systems: A Mobile Toolkit for Extracting Time-of-Day Fluctuations of Cognitive Performance. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 47. Google ScholarDigital Library
- Tilman Dingler, Dominik Weber, Martin Pielot, Jennifer Cooper, Chung-Cheng Chang, and Niels Henze. 2017b. Language learning on-the-go: opportune moments and design of mobile microlearning sessions. In Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 28. Google ScholarDigital Library
- Anastasios A Economides. 2008. Context-aware mobile learning. In World Summit on Knowledge Society. Springer, 213--220.Google Scholar
- Nitza Geri, Amir Winer, and Beni Zaks. 2017. Challenging the six-minute myth of online video lectures: Can interactivity expand the attention span of learners? Online Journal of Applied Knowledge Management 5, 1 (2017), 101--111.Google ScholarCross Ref
- Robert Godwin-Jones. 2011. Mobile apps for language learning. (2011).Google Scholar
- Jin Huang, Chun Yu, Yuntao Wang, Yuhang Zhao, Siqi Liu, Chou Mo, Jie Liu, Lie Zhang, and Yuanchun Shi. 2014. FOCUS: enhancing children's engagement in reading by using contextual BCI training sessions. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems. ACM, 1905--1908. Google ScholarDigital Library
- Jantina Huizenga, Wilfried Admiraal, Sanne Akkerman, and G ten Dam. 2009. Mobile game-based learning in secondary education: engagement, motivation and learning in a mobile city game. Journal of Computer Assisted Learning 25, 4 (2009), 332--344.Google ScholarCross Ref
- Shamsi T Iqbal and Eric Horvitz. 2007. Disruption and recovery of computing tasks: field study, analysis, and directions. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 677--686. Google ScholarDigital Library
- Ann Jones, Kim Issroff, Eileen Scanlon, Gill Clough, Patrick McAndrew, and Canan Blake. 2006. Using mobile devices for learning in informal settings: is it motivating? (2006).Google Scholar
- Satu Jumisko-Pyykkö and Teija Vainio. 2010. Framing the context of use for mobile HCI. International journal of mobile human computer interaction (IJMHCI) 2, 4 (2010), 1--28. Google ScholarDigital Library
- N Kleitman, S Titelbaum, and P Feiveson. 1938. The effect of body temperature on reaction time. American Journal of Physiology-Legacy Content 121, 2 (1938), 495--501.Google ScholarCross Ref
- Ning-Han Liu, Cheng-Yu Chiang, and Hsuan-Chin Chu. 2013. Recognizing the degree of human attention using EEG signals from mobile sensors. Sensors 13, 8 (2013), 10273--10286.Google ScholarCross Ref
- Akhil Mathur, Nicholas D Lane, and Fahim Kawsar. 2016. Engagement-aware computing: Modelling user engagement from mobile contexts. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 622--633. Google ScholarDigital Library
- Richard E Mayer, Hyunjeong Lee, and Alanna Peebles. 2014. Multimedia learning in a second language: A cognitive load perspective. Applied Cognitive Psychology 28, 5 (2014), 653--660.Google ScholarCross Ref
- Tadashi Okoshi, Jin Nakazawa, and Hideyuki Tokuda. 2014. Attelia: Sensing user's attention status on smart phones. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. ACM, 139--142. Google ScholarDigital Library
- Daniel L Schacter. 1999. The seven sins of memory: Insights from psychology and cognitive neuroscience. American psychologist 54, 3 (1999), 182.Google Scholar
- Caroline Steel. 2012. Fitting learning into life: Language students' perspectives on benefits of using mobile apps. In ascilite. 875-880.Google Scholar
- Deborah Tatar, Jeremy Roschelle, Phil Vahey, and William R Penuel. 2003. Handhelds go to school: Lessons learned. Computer 9 (2003), 30--37. Google ScholarDigital Library
- David A Washburn and R Thompson Putney. 2001. Attention and task difficulty: when is performance facilitated? Learning and Motivation 32, 1 (2001), 36--47.Google ScholarCross Ref
- Xiang Xiao and Jingtao Wang. 2017. Undertanding and Detecting Divided Attention in Mobile MOOC Learning. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 2411--2415. Google ScholarDigital Library
- Robert M Yerkes and John D Dodson. 1908. The relation of strength of stimulus to rapidity of habit-formation. Journal of comparative neurology and psychology 18, 5 (1908), 459--482.Google ScholarCross Ref
- Bin Zou and Jiaying Li. 2015. Exploring Mobile Apps for English Language Teaching and Learning. Research-publishing. net (2015).Google ScholarCross Ref
Index Terms
- Informing the Design of User-adaptive Mobile Language Learning Applications
Recommendations
Why Did you Stop? - Investigating Origins and Effects of Interruptions during Mobile Language Learning
MuC '21: Proceedings of Mensch und Computer 2021The technological advances of smartphones facilitate the transformation of learning from the classroom to an activity that can happen anywhere and anytime. While micro-learning fosters ubiquitous learning, this flexibility comes at the cost of having ...
Will mobile learning change language learning?
The use of mobile phones and other portable devices is beginning to have an impact on how learning takes place in many disciplines and contexts, including language learning. Learners who are not dependent on access to fixed computers can engage in ...
Personalization in Context-aware Ubiquitous Learning-Log System
WMUTE '12: Proceedings of the 2012 IEEE Seventh International Conference on Wireless, Mobile and Ubiquitous Technology in EducationThis paper describes a ubiquitous learning log system called SCROLL (System for Capturing and Reminding of Learning Log). Learning log stands for the log of knowledge or learning experience acquired ubiquitously. This study primarily exploits a ...
Comments